Permutation-Robust Structure for ICA-Based Blind Source Extraction

In this paper, we investigate a new blind source separation (BSS) structure from a permutation-robustness viewpoint, to mitigate the permutation problem which commonly arises in frequency-domain independent component analysis (ICA). Permutation robustness means that how much the BSS method is not affected under a certain probability of arising permutation, unlike the conventional permutation-solving approaches. We address to analyze our previously proposed BSS architecture, so called blind spatial subtraction array (BSSA). In BSSA, source extraction is achieved by subtracting the power spectrum of the estimated noise via ICA from the power spectrum of partly speech-enhanced signal via delay-and-sum (DS) procedure. Indeed BSSA partially involves permutation problem in the ICA-based noise estimator part. However, BSSA can efficiently reduce the negative affection of the permutation owing to the over-subtraction in the spectral subtraction and defocusing properties in DS. Experiments using artificial and real-recording-based simulations reveal that the proposed method outperforms the conventional ICA.